Some imaging modality systems such as X-ray, MR, CT scan and Ultrasound systems produce noisy images under some conditions that render identification of anatomical features and their boundaries (a process termed segmentation) difficult. Specifically, X-ray angiographic left ventricular image acquisition and analysis using cardiac image segmentation, is often difficult because of image noise derived from heart movement, patient movement, environmental effects and X-ray imaging artifacts, for example. In this application, imaging conditions typically cannot be optimized for individual image frames. However, images are processed notwithstanding image clutter and noise including environmental effects such as low contrast and non-uniform illumination, fadeout, glare, and loss of focus, for example.
Known common medical image segmentation functions typically fail to adequately identify anatomical features in the presence of image noise. FIGS. 1A and 1B illustrate image Segmentation to identify an anatomical feature using known different processes including, a first (graph cut) process providing curves 104 and a second (snake) process providing curves 107. The known processes fail to correctly identify the anatomical feature boundary in the noisy X-ray images of FIGS. 1A and 1B. The second (snake) process providing curves 107 suffers from a known local minimal problem and fails to correctly identify the anatomical feature boundary and the first (graph cut) process providing curves 104 fails to identify the boundary in the X-ray images because of low contrast. Further, use of Active Shape Models (ASMs) which are statistical models learned from examples to capture shape variations in an object, may perform more robustly in image interpretation. However, a boundary finding process employed by ASMs is deterministic, requiring a model to be initialized sufficiently close to the object to converge and may be prone to error due to local minima. Other known image feature boundary identification systems exhibit difficulty in processing noisy image data. A system according to invention principles addresses these deficiencies and related problems.